A double-parameter scaling Broyden-Fletcher-Goldfarb-Shanno method based on minimizing the measure function of Byrd and Nocedal for unconstrained optimization
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Publication:1670017
DOI10.1007/s10957-018-1288-3zbMath1398.49025OpenAlexW2801253117WikidataQ129884028 ScholiaQ129884028MaRDI QIDQ1670017
Publication date: 4 September 2018
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10957-018-1288-3
global convergencenonlinear programmingnumerical comparisonsmeasure function of Byrd and Nocedalscaling BFGS method
Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37)
Related Items (3)
Nonmonotone diagonally scaled limited-memory BFGS methods with application to compressive sensing based on a penalty model ⋮ A variation of Broyden class methods using Householder adaptive transforms ⋮ Diagonally scaled memoryless quasi-Newton methods with application to compressed sensing
Uses Software
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